Digital cameras have an ISO rating identifying their level of sensitivity to light.
For most digital cameras, a default ISO 100 setting is normal although some digital cameras have default ISO settings as low as fifty (50). An exemplary digital camera board has an ISO range between fourteen (14) and nine-hundred and ten (910) and a default ISO 50 setting. In high-end digital, single-lens reflex (SLR) cameras, even higher ISO settings are available. For example, in some digital SLR cameras, the default ISO setting can be set to 200, 400, 800 or even 3200.
When increasing the ISO setting of a digital camera and hence, its sensitivity to light, the output of the digital camera sensor is amplified and so less light is needed during image capture. Unfortunately, undesired noise is also amplified resulting in more grainy pictures. Noise in general is introduced into images by both the digital camera sensor and the digital camera signal amplifier as a result of particular shooting conditions, image compression etc.
In examining noise in images captured by digital cameras at high ISO settings, it has been found that noise generally takes two forms, namely luminance noise and chromanoise. Luminance noise gives images a grainy look when presented on an electronic display screen but is usually not visible when the images are printed. Chromanoise appears as random red and blue pixels and is typically less visible in images both on-screen and when printed. Chromanoise is most visible in smooth and/or dark areas of images and is more visible in red (R) and blue (B) channels. Chromanoise in the red and blue channels tends to contain more low and very low frequency components due to demosaicing and interpolation. Chromanoise also adversely affects color saturation and contrast.
Many methods to remove noise from images have been considered. Some noise reduction methods, such as for example, anisotropic diffusion, assume that white noise has been added to images and compensate for image degradation on the basis of this assumption. Unfortunately, noise in images captured by digital cameras having high ISO settings is not accurately modeled by this noise assumption and thus, these noise reducing methods have proven to be unsatisfactory.
Wavelet methods for removing noise from images have gained in popularity. The Donoho-Johnstone method is a popular and efficient denoising method incorporating wavelets and makes use of soft or hard thresholds. During denoising of an image, the image is wavelet decomposed to generate wavelet coefficients representing the image detail. All of the wavelet coefficients below threshold levels are reduced to zero. Wavelet coefficients exceeding the threshold levels are either unmodified (hard) or reduced (soft). This method has proven to be most successful when the noise in the image generally conforms to expected Gaussian white noise. Unfortunately, this method requires the arbitrary a priori selection of the threshold levels. As a result, depending on the selected threshold levels, if the noise is not white noise, detail in the image may be lost, an insufficient amount of noise may be removed from the image or the noise may be improperly removed from the image.
Other more sophisticated noise reducing algorithms have been considered but these noise reducing algorithms are expensive in terms of processing time. Many of these methods, similar to the anisotropic diffusion method referred to above, incorporate assumed noise models and thus, unsuccessfully address noise when the noise does not accurately fit within the noise models.
Still other techniques for reducing noise in images have been considered. For example, U.S. Pat. No. 6,163,619 to Maruo discloses a method in which an input digital image of an object is subjected to a Wavelet transform. An image energy quantity for a combined area of X-axis high pass information and Y-axis high pass information contained in the image data resulting from the Wavelet transform, is calculated. The object is deemed acceptable or faulty depending on whether the image energy quantity is below or above a given value.
U.S. Pat. No. 6,836,569 to Le Pennac et al. discloses a method and apparatus for processing an n-dimensional digitized signal using foveal processing, which constructs a sparse signal representation by taking advantage of the geometrical regularity of signal structures. Foveal coefficients are computed with one-dimensional inner products along trajectories of an n-directional trajectory list. A trajectory finder computes the n-directional trajectory list from the input n-dimensional signals, in order to choose optimal locations to compute the foveal coefficients. From the foveal coefficients, a foveal reconstruction processor recovers a signal approximation which has the same geometrical structures as the input signal along the trajectories and which is regular away from the trajectories. A foveal residue can be calculated as a difference with the input signal. A bandlet processor decorrelates the foveal coefficients by applying invertible linear operators along each trajectory. Bandlet coefficients are inner products between the signal and n-dimensional bandlet vectors elongated along the trajectories. A geometric processor computes geometric coefficients by decorrelating the coordinates of the trajectories with linear operators, to take advantage of their geometrical regularity. Setting small bandlet coefficients and small geometric coefficients to zero yields the sparse signal representation.
U.S. Patent Application Publication No. 2003/0095206 to Wredenhagen et al. discloses a method for reducing noise in an image. During the method, a plurality of pixels along a predetermined contour is compared with a plurality of predefined patterns. The patterns represent visually significant patterns possible along the contour. A filter is selected from a predefined set of filters in accordance with the results of the comparison.
U.S. Patent Application Publication No. 2004/0008904 to Lin et al. discloses a process for removing noise in an image by wavelet thresholding utilizing a discrete wavelet transform that decomposes the image into different resolution levels. A thresholding function is then applied in different resolution levels with different threshold values to eliminate insignificant wavelet coefficients which mainly correspond to noise in the image. An inverse discrete wavelet transform is applied to generate a noise-reduced image. The threshold values are based on the relationships between the noise standard deviations at different decomposition levels in the wavelet domain and the noise standard deviation of the image.
U.S. Patent Application Publication No. 2004/0260169 to Sternnickel discloses a two-part method for non-linear de-noising (NLD) of magneto cardiograph or electrocardiograph time series signals by performing local projections in the reconstructed state space using a wavelet transform to identify and describe deterministic structures. Subspaces generated by deterministic processes are located and separated independently of their sources.
U.S. Patent Application Publication No. 2004/0268096 to Master et al. discloses a digital imaging apparatus such as a digital camera, scanner, printer or dry copier having an optical sensor, an analog-to-digital converter, a plurality of computational elements, and an interconnection network. The optical sensor converts an object image into a detected image, which is then converted to digital image information by the analog-to-digital converter. The plurality of computational elements comprises a first computational element having a first fixed architecture and a second computational element having a second, different fixed architecture. The interconnection network is capable of providing a processed digital image from the digital image information by configuring and reconfiguring the computational elements in order to perform a plurality of different imaging functions.
U.S. Patent Application Publication No. 2005/0100237 to Kong et al. discloses a method of filtering pixels in an image, by first partitioning the image into blocks. Edge blocks are then identified. A variance of intensity for each pixel in each edge block is subsequently determined. Each pixel in each edge block is then filtered with a filter that is dependant on the intensity variance of the pixel.
U.S. Patent Application Publication No. 2005/40100241 to Kong et al. discloses a method for reducing artefacts in an input image. A variance image is generated from the input image. The input image is partitioned into a plurality of blocks of pixels. A set of classifications is defined and includes smooth, texture, and edge classes. A particular class is assigned to each block of pixels of the input image according to the variance image thereby to generate smooth blocks, texture blocks, and edge blocks. A fuzzy filter is applied to each pixel of each edge block.
U.S. Patent Application Publication No. 2005/0207660 to Edgar discloses a method for removing artefacts to restore a block of data. During the method, one or more original transform coefficients of the data block are received and the original transform coefficients of the data block are quantized. An artefact reduction process is applied to the quantized original transform coefficients and one or more quantized data values representing the transform coefficients as altered by the artefact reduction process is adjusted, if necessary. The artefact reduction process is reapplied and the quantized data values are readjusted, enabling restoration of the block of data.
U.S. Patent Application Publication No. 2005/0265633 to Piacentino et al. discloses a video processor that uses a low latency pyramid processing technique for fusing images from multiple sensors. The images from the multiple sensors are enhanced, warped into alignment, and then fused with one another in a manner that enables the fusing to occur within a single frame of video, i.e., sub-frame processing. The sub-frame processing results in a sub-frame delay between the moment of image capture and subsequent fused image display.
U.S. Patent Application Publication No. 2005/0276515 to Shekter discloses an apparatus for analyzing the broadband noise content of a digital image. The apparatus comprises means for automatically identifying regions of originally constant color in the image by analyzing the variance of pixel values of regions of the image. The apparatus further comprises means for automatically detecting and discarding constant color regions deemed to be unrepresentative of the true noise content of the image, including under-exposed and over-exposed regions. Selected constant color regions are then analyzed to generate a parametric or non-parametric model of the noise in the image, including frequency characteristics within and between channels and other characteristics such as phase, which might describe structured noise.
Although image noise reducing techniques are available, improvements are desired. It is therefore an object of the present invention to provide a novel method, apparatus and computer-readable medium embodying a computer program for reducing noise in an image using wavelet decomposition.